Property Pricing SaaS for Real Estate Agents

Real estate agents spend hours preparing pricing presentations. But the harder part often comes when a seller wants a number the market doesn't support — how do you clearly show the risk behind that recommendation?

Generic AI tools didn't solve that problem. They could produce fluent answers, but there was no audit trail, no confidence signal and no reliable way to understand how the final number was reached.

I built a subscription AI SaaS on Bubble.io that turns an agent's property details and comparable sales into a defensible pricing report in minutes. Instead of relying on a single AI prompt, I engineered a 15-module reasoning pipeline powered by Anthropic Claude, where every step is independently prompted, validated, and logged.

The result is more than a suggested price. Each report provides a defensive and aspirational price range, a High, Moderate, or Limited confidence grade, and the reasoning behind every number — then turns the analysis into branded agent and client PDFs through PDFMonkey.

Making the AI reliable inside a no-code platform was the next challenge. I built recursive background workflows for long-running generation, a JavaScript parsing and validation layer for model output and cross-module coherence checks that flag reports for human review.

Behind the scenes, token usage and cost are tracked per module, while admins can switch AI models or disable generation without a redeploy.

The platform also includes Stripe subscriptions with Basic, Pro, and Elite tiers, a 7-day trial, usage limits, and an admin console for agents, revenue, content audits, and AI controls.

The result is a production-grade, auditable AI product serving agents across Canada and the United States — giving them a structured way to turn property data into pricing recommendations they can actually explain and stand behind.